SAR Image Denoising Using Wavelet Transform

In this article, I will demonstrate how to implement SAR image denoising using wavelet transform. The approach involves applying wavelet decomposition to separate noise components from meaningful image data, followed by thresholding techniques to remove noise while preserving important features. First, I will provide a comprehensive experimental report detailing the objectives, methodology involving wavelet coefficient thresholding, and quantitative results comparing signal-to-noise ratio improvements. The implementation typically utilizes functions like wavedec2 for 2D wavelet decomposition and wthresh for applying soft or hard thresholding to wavelet coefficients. Then, I will present experimental test images that validate the denoising effectiveness, showing visual comparisons between original and processed images. By employing wavelet transform methods with proper threshold selection algorithms, we can effectively reduce image noise while maintaining edge information and texture details, significantly enhancing image quality and clarity for SAR applications.